General Program Objective: To empower participants to acquire the theoretical knowledge and practical skills necessary to apply Artificial Intelligence technologies in business environments, analyze data, and build effective Machine Learning models that support decision-making and organizational innovation.

Detailed Program Objectives: By the end of this training program, participants will be able to:

• Understand the fundamental concepts, history, and modern applications of Artificial Intelligence.

• Differentiate between types of Machine Learning algorithms and their practical applications.

• Develop Machine Learning models and manage the Machine Learning Lifecycle (ML Lifecycle).

• Grasp the techniques of Natural Language Processing (NLP), Computer Vision (CV), and Deep Learning.

• Utilize tools and platforms for building AI models, including:

  • Python, Scikit-learn, TensorFlow / PyTorch (Introductory Level)

• Utilize Generative AI tools effectively.

• Analyze data, extract patterns, and build predictive indicators.

• Understand the ethical principles of Artificial Intelligence and digital governance.

• Design deployable AI solutions within the organization.

• Develop documentation and business cases (Use Cases) suitable for Business Intelligence and digital transformation.

Module 1: Introduction to Artificial Intelligence

• Definition of Artificial Intelligence and its historical evolution.

• Key applications across sectors (Healthcare, Finance, Manufacturing, Education, Government Services).

• Differences between AI, Machine Learning, and Deep Learning.

• Generative AI (GenAI) and its revolution in the job market.

Module 2: Fundamentals of Machine Learning (ML)

• Key concepts: Data, Features, Training, and Testing.

• Types of Machine Learning:

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning (Overview)

• Popular Algorithms: Decision Trees, Random Forest, SVM, K-Means.

• Model evaluation and performance metrics: Accuracy, Precision, Recall, F1-Score.

Module 3: Data Analysis and Model Building

• Data collection and Data Cleaning.

• Data Preprocessing.

• Feature Engineering and selection.

• Data splitting and optimizing model performance.

• Practical Python tools for data analysis (Pandas, NumPy, Matplotlib).

Module 4: Deep Learning – Introductory Level

• Core concepts of Neural Networks.

• Network layers and the training process.

• Introduction to TensorFlow and PyTorch.

• Key use cases.

Module 5: Natural Language Processing (NLP)

• Text analysis and classification.

• Language Models.

• Introduction to Generative AI and its uses in content creation.

• Applications of ChatGPT and LLMs in business.

Module 6: Computer Vision (CV)

• Core concepts of image and video analysis.

• CV models: CNN, Image Classification, Object Detection.

• Practical applications: Security, Healthcare, Transportation, Manufacturing.

Module 7: AI Tools and Techniques in Business

• Automated Machine Learning (AutoML) platforms.

• Integration of AI and business processes (BPM + AI).

• Translating use cases into practical AI solutions.

• Formulating an enterprise AI Roadmap.

Module 8: AI Ethics and Governance

• Model-related risks: Bias, Privacy, and Transparency.

• International principles for responsible AI governance.

• Cybersecurity in intelligent environments.

• Institutional compliance frameworks.

Module 9: Practical Capstone Project

• Selecting a real-world organizational Use Case.

• Data analysis and building a Minimum Viable Product (MVP).

• Presenting the final project.